Kibana MCP server

Kibana is a data visualization and exploration platform for logs, metrics, and time-series analytics built on top of Elasticsearch. With this MCP server, AI agents can create dashboards, build visualizations, explore data, manage spaces, and configure alerts through natural language commands.

Setting up an MCP server

This article covers the standard steps for creating an MCP server in AI Gateway and connecting it to an AI client. The steps are the same for every integration — application-specific details (API credentials, OAuth endpoints, and scopes) are covered in the Authentication section on this page.

Before you begin

You'll need:

• Access to AI Gateway with permission to create MCP servers.
• API credentials for the application you're connecting (see the Authentication section on this page for what to collect).

Create an MCP server

Find the app in the catalog

1. Sign in to AI Gateway and select App Catalog from the left navigation.
2. Search for the application you want to connect, then select it from the catalog.
3. Select Create MCP Server to start the wizard.

App Configuration

Confirm the Base URL for the API, then, under Tools, select the endpoints you want to expose. Select Next.

MCP Server Setup

1. Enter a Name for your server — something descriptive that identifies both the application and its purpose.
2. Enter a Description so your team knows what the server is for.
3. Set the log level: choose Production Mode for terser logs, or Non-Production Mode for more verbose logs that can help with debugging.
4. Select Next.

Authentication

Enter the authentication details for the application. This varies by service — see the Authentication section on this page for the specific credentials, OAuth URLs, and scopes to use.

Review

Look over the summary of your MCP server configuration, then select Create & Deploy. AI Gateway provisions the server and provides a server URL you'll use when configuring your AI client.

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Connect to an AI client

Once your server is deployed, you'll need to add it to the AI client your team uses. Select your client for setup instructions:

• Claude Desktop
• Claude Code
• Cursor
• VS Code
• Windsurf

Tips

• You can create multiple MCP servers for the same application — for example, a read-only server for reporting agents and a read-write server for automation workflows.
• If you're unsure which OAuth scopes to request, start with the minimum read-only set and add write scopes only when needed. Most application pages include scope recommendations.

Authentication

Kibana supports API key authentication for programmatic access. Generate API keys from Kibana > Stack Management > API Keys.

Value	Setting
API key header	Authorization: ApiKey {encoded_api_key}

Available tools

The Kibana MCP server exposes dashboard management, visualization creation, data exploration, space management, and alerting APIs.

Tool	Purpose
Dashboard Management	Create, update, and delete dashboards; manage dashboard layouts; export configurations
Visualization API	Build charts (bar, line, pie, heat map); manage aggregations; customize visualization settings
Discover & Saved Searches	Explore data with filters; save search queries; export search results; manage index patterns
Spaces & Organization	Create and manage spaces; move objects between spaces; configure space-level permissions
Saved Objects	Export and import dashboards; bulk update objects; resolve import conflicts
Alerting Rules	Create threshold and query-based alerts; configure alert actions; manage alert lifecycle

Tips

Start with a clear purpose for each dashboard and group related visualizations logically.

Use meaningful titles and descriptions and avoid dashboard clutter with too many panels.

Choose the right visualization type for your data (line for trends, bar for comparison, pie for composition).

Keep aggregations simple and understandable, and use appropriate time ranges.

Create separate index patterns for different data sources and use consistent naming conventions.

Configure the time field correctly for time-series data.

Set meaningful threshold values that reduce alert fatigue.

Test alerts in dev before production.

Use appropriate notification channels for different alert severities.

Include helpful context in alert messages.

Organize spaces by team or business function and limit access based on roles.

Use consistent naming for spaces and document what each space contains.

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